FastICA Algorithm for Blind Signal Separation
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This document introduces the FastICA algorithm for blind signal separation and demonstrates its application on four images containing mixed interference. The implementation involves loading the multichannel image data as input signals, where each pixel sequence is treated as an observed mixture. The core algorithm performs centering and whitening preprocessing to normalize the data, followed by iterative fixed-point optimization to maximize non-Gaussianity through negentropy approximation. Key functions include symmetric orthogonalization for simultaneous independent component extraction and contrast function optimization using hyperbolic tangent nonlinearities. Post-separation, the algorithm reconstructs source signals by inverting the mixing matrix estimation, resulting in displayed separated images that reveal underlying source characteristics. This approach enables effective signal disentanglement for deeper image analysis and pattern recognition.
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